Instructions to use jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1") model = AutoModelForMaskedLM.from_pretrained("jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- pytorch_model.bin +1 -1
- runs/May22_14-41-26_30226d4d9928/1684766810.9625237/events.out.tfevents.1684766810.30226d4d9928.21320.4 +3 -0
- runs/May22_14-41-26_30226d4d9928/events.out.tfevents.1684766810.30226d4d9928.21320.3 +3 -0
- runs/May22_14-41-26_30226d4d9928/events.out.tfevents.1684766827.30226d4d9928.21320.5 +3 -0
- tokenizer.json +6 -1
- training_args.bin +1 -1
pytorch_model.bin
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tokenizer.json
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training_args.bin
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